{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c3afa009-cc7a-436b-be99-8f1f403af84a",
   "metadata": {},
   "source": [
    "# Image to Video Generation with Stable Video Diffusion\n",
    "\n",
    "Stable Video Diffusion (SVD) Image-to-Video is a diffusion model that takes in a still image as a conditioning frame, and generates a video from it. In this tutorial we consider how to convert and run Stable Video Diffusion using OpenVINO.\n",
    "We will use [stable-video-diffusion-img2video-xt](https://huggingface.co/stabilityai/stable-video-diffusion-img2vid-xt) model as example. Additionally, to speedup video generation process we apply [AnimateLCM](https://arxiv.org/abs/2402.00769) LoRA weights and run optimization with [NNCF](https://github.com/openvinotoolkit/nncf/).\n",
    "\n",
    "#### Table of contents:\n",
    "\n",
    "- [Prerequisites](#Prerequisites)\n",
    "- [Convert Model to OpenVINO Intermediate Representation](#Convert-Model-to-OpenVINO-Intermediate-Representation)\n",
    "- [Prepare Inference Pipeline](#Prepare-Inference-Pipeline)\n",
    "- [Run Video Generation](#Run-Video-Generation)\n",
    "    - [Select Inference Device](#Select-Inference-Device)\n",
    "- [Quantization](#Quantization)\n",
    "    - [Prepare calibration dataset](#Prepare-calibration-dataset)\n",
    "    - [Run Hybrid Model Quantization](#Run-Hybrid-Model-Quantization)\n",
    "    - [Run Weight Compression](#Run-Weight-Compression)\n",
    "    - [Compare model file sizes](#Compare-model-file-sizes)\n",
    "    - [Compare inference time of the FP16 and INT8 pipelines](#Compare-inference-time-of-the-FP16-and-INT8-pipelines)\n",
    "- [Interactive Demo](#Interactive-Demo)\n",
    "\n",
    "\n",
    "### Installation Instructions\n",
    "\n",
    "This is a self-contained example that relies solely on its own code.\n",
    "\n",
    "We recommend  running the notebook in a virtual environment. You only need a Jupyter server to start.\n",
    "For details, please refer to [Installation Guide](https://github.com/openvinotoolkit/openvino_notebooks/blob/latest/README.md#-installation-guide).\n",
    "\n",
    "<img referrerpolicy=\"no-referrer-when-downgrade\" src=\"https://static.scarf.sh/a.png?x-pxid=5b5a4db0-7875-4bfb-bdbd-01698b5b1a77&file=notebooks/stable-video-diffusion/stable-video-diffusion.ipynb\" />\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "117deb8f-bae0-4623-98b6-a2409d6eb0cc",
   "metadata": {},
   "source": [
    "## Prerequisites\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "21709ff7-a138-4256-9d2c-ba789a897162",
   "metadata": {},
   "outputs": [],
   "source": [
    "%pip install -q \"torch>=2.1\" \"diffusers>=0.25\" accelerate  \"peft>=0.15.0\" \"transformers\" Pillow opencv-python tqdm  \"gradio>=4.19\" safetensors --extra-index-url https://download.pytorch.org/whl/cpu\n",
    "%pip install -q \"datasets<4.0.0\" \"nncf>=2.16.0\"\n",
    "%pip install -qU --pre \"openvino>=2025.1.0\" --extra-index-url https://storage.openvinotoolkit.org/simple/wheels/nightly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e29229d-821d-4367-8f91-ad8375a38895",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import requests\n",
    "\n",
    "if not Path(\"lcm_scheduler.py\").exists():\n",
    "    lcm_scheduler_url = \"https://huggingface.co/spaces/wangfuyun/AnimateLCM-SVD/raw/main/lcm_scheduler.py\"\n",
    "\n",
    "    r = requests.get(lcm_scheduler_url)\n",
    "\n",
    "    with open(\"lcm_scheduler.py\", \"w\") as f:\n",
    "        f.write(r.text)\n",
    "\n",
    "if not Path(\"skip_kernel_extension.py\").exists():\n",
    "    r = requests.get(\n",
    "        url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/skip_kernel_extension.py\",\n",
    "    )\n",
    "    open(\"skip_kernel_extension.py\", \"w\").write(r.text)\n",
    "\n",
    "if not Path(\"notebook_utils.py\").exists():\n",
    "\n",
    "    r = requests.get(\n",
    "        url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\",\n",
    "    )\n",
    "    open(\"notebook_utils.py\", \"w\").write(r.text)\n",
    "\n",
    "if not Path(\"ov_stable_video_diffusion_helper.py\").exists():\n",
    "\n",
    "    r = requests.get(\n",
    "        url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-video-diffusion/ov_stable_video_diffusion_helper.py\",\n",
    "    )\n",
    "    open(\"ov_stable_video_diffusion_helper.py\", \"w\").write(r.text)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "92eb1bd8-d933-4e44-8167-6690ff11dfbc",
   "metadata": {},
   "source": [
    "## Convert Model to OpenVINO Intermediate Representation\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "OpenVINO supports PyTorch models via conversion into Intermediate Representation (IR) format. We need to provide a model object, input data for model tracing to `ov.convert_model` function to obtain OpenVINO `ov.Model` object instance. Model can be saved on disk for next deployment using `ov.save_model` function.\n",
    "\n",
    "Stable Video Diffusion consists of 3 parts:\n",
    "\n",
    "* **Image Encoder** for extraction embeddings from the input image.\n",
    "* **U-Net** for step-by-step denoising video clip.\n",
    "* **VAE** for encoding input image into latent space and decoding generated video.\n",
    "\n",
    "Let's convert each part.\n",
    "\n",
    "`ov_stable_video_diffusion_helper.py` contains helper function for model conversion `convert_stable_video_diffusion`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "60e89c71-4cf6-4e87-b788-8fa5265bca71",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2025-04-23 21:00:08.275703: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2025-04-23 21:00:08.288865: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
      "E0000 00:00:1745427608.302868 2409691 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "E0000 00:00:1745427608.307034 2409691 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
      "2025-04-23 21:00:08.322365: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "# Read more about telemetry collection at https://github.com/openvinotoolkit/openvino_notebooks?tab=readme-ov-file#-telemetry\n",
    "from notebook_utils import collect_telemetry\n",
    "\n",
    "collect_telemetry(\"stable-video-diffusion.ipynb\")\n",
    "\n",
    "from ov_stable_video_diffusion_helper import convert_stable_video_diffusion\n",
    "\n",
    "# Uncomment the line to see model conversion code\n",
    "# ??convert_stable_video_diffusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "779831aa-d64b-4d07-b5c0-8a98b56fe583",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Passed `torch_dtype` torch.float32 is not a `torch.dtype`. Defaulting to `torch.float32`.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "601a025f6f774be09925aa558a8415c7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading pipeline components...:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.\n",
      "/home/ea/work/py311/lib/python3.11/site-packages/transformers/models/clip/modeling_clip.py:243: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if not interpolate_pos_encoding and (height != self.image_size or width != self.image_size):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Image Encoder successfully converted to IR and saved to model/image_encoder.xml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/upsampling.py:147: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n",
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/upsampling.py:162: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if hidden_states.shape[0] >= 64:\n",
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/upsampling.py:173: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if hidden_states.numel() * scale_factor > pow(2, 31):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE Decoder successfully converted to IR and saved to model/vae_encoder.xml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/downsampling.py:136: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n",
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/downsampling.py:145: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  assert hidden_states.shape[1] == self.channels\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "VAE Encoder successfully converted to IR and saved to model/vae_encoder.xml\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/models/unets/unet_spatio_temporal_condition.py:391: TracerWarning: Converting a tensor to a Python boolean might cause the trace to be incorrect. We can't record the data flow of Python values, so this value will be treated as a constant in the future. This means that the trace might not generalize to other inputs!\n",
      "  if any(s % default_overall_up_factor != 0 for s in sample.shape[-2:]):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UNet successfully converted to IR and saved to model/unet.xml\n"
     ]
    }
   ],
   "source": [
    "convert_stable_video_diffusion()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "48ecf8f3-ffc4-4b97-ac7d-d3ea9bb3fd75",
   "metadata": {},
   "source": [
    "## Prepare Inference Pipeline\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "The code bellow implements `OVStableVideoDiffusionPipeline` class for running video generation using OpenVINO. The pipeline accepts input image and returns the sequence of generated frames\n",
    "The diagram below represents a simplified pipeline workflow.\n",
    "\n",
    "![svd](https://github.com/openvinotoolkit/openvino_notebooks/assets/29454499/a5671c5b-415b-4ae0-be82-9bf36527d452)\n",
    "\n",
    "The pipeline is very similar to [Stable Diffusion Image to Image Generation pipeline](../stable-diffusion-text-to-image/stable-diffusion-text-to-image.ipynb) with the only difference that Image Encoder is used instead of Text Encoder. Model takes input image and random seed as initial prompt. Then image encoded into embeddings space using Image Encoder and into latent space using VAE Encoder and passed as input to U-Net model. Next, the U-Net iteratively *denoises* the random latent video representations while being conditioned on the image embeddings. The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm for next iteration in generation cycle. This process repeats the given number of times and, finally, VAE decoder converts denoised latents into sequence of video frames."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1b073909-aa5c-4252-bff4-0a7e34a6c983",
   "metadata": {},
   "outputs": [],
   "source": [
    "from ov_stable_video_diffusion_helper import OVStableVideoDiffusionPipeline\n",
    "\n",
    "# Uncomment the line to see pipeline code\n",
    "# ??OVStableVideoDiffusionPipeline"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "72845107-4b68-4f36-9ffb-9a9c31cca63c",
   "metadata": {},
   "source": [
    "## Run Video Generation\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "### Select Inference Device\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a9c9ba7-8234-44ac-bbb3-708e3bea5640",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bb90637364c34860b6b7292b98272f4d",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Device:', index=1, options=('CPU', 'AUTO'), value='AUTO')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from notebook_utils import device_widget\n",
    "\n",
    "device = device_widget()\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bbaf9418-bbff-4275-b43b-b1c14cc92aca",
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import CLIPImageProcessor\n",
    "from pathlib import Path\n",
    "from diffusers.utils import load_image, export_to_video\n",
    "from ov_stable_video_diffusion_helper import VAE_ENCODER_PATH, VAE_DECODER_PATH, MODEL_DIR, UNET_PATH, IMAGE_ENCODER_PATH\n",
    "from lcm_scheduler import AnimateLCMSVDStochasticIterativeScheduler\n",
    "import openvino as ov\n",
    "\n",
    "# Load the conditioning image\n",
    "\n",
    "image_path = Path(\"rocket.png\")\n",
    "if not image_path.exists():\n",
    "    image = load_image(\"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/diffusers/svd/rocket.png?download=true\")\n",
    "    image = image.resize((512, 256))\n",
    "    image.save(image_path)\n",
    "else:\n",
    "    image = load_image(str(image_path))\n",
    "\n",
    "core = ov.Core()\n",
    "\n",
    "vae_encoder = core.compile_model(VAE_ENCODER_PATH, device.value)\n",
    "image_encoder = core.compile_model(IMAGE_ENCODER_PATH, device.value)\n",
    "unet = core.compile_model(UNET_PATH, device.value)\n",
    "vae_decoder = core.compile_model(VAE_DECODER_PATH, device.value)\n",
    "scheduler = AnimateLCMSVDStochasticIterativeScheduler.from_pretrained(MODEL_DIR / \"scheduler\")\n",
    "feature_extractor = CLIPImageProcessor.from_pretrained(MODEL_DIR / \"feature_extractor\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "3ba0d612-2ba4-4297-94cb-9ca54f5c14a3",
   "metadata": {},
   "source": [
    "Now, let's see model in action.\n",
    "> Please, note, video generation is memory and time consuming process. For reducing memory consumption, we decreased input video resolution to 576x320 and number of generated frames that may affect quality of generated video. You can change these settings manually providing `height`, `width` and `num_frames` parameters into pipeline. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c722800-7800-4a81-8a39-369dd182237e",
   "metadata": {},
   "outputs": [],
   "source": [
    "ov_pipe = OVStableVideoDiffusionPipeline(vae_encoder, image_encoder, unet, vae_decoder, scheduler, feature_extractor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "02b62761-35d4-46be-a7eb-bdc8774de7cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da9fd4055d3d4dc58708a5982a05f848",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/configuration_utils.py:141: FutureWarning: Accessing config attribute `unet` directly via 'OVStableVideoDiffusionPipeline' object attribute is deprecated. Please access 'unet' over 'OVStableVideoDiffusionPipeline's config object instead, e.g. 'scheduler.config.unet'.\n",
      "  deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "denoise currently\n",
      "tensor(128.5637)\n",
      "denoise currently\n",
      "tensor(13.6784)\n",
      "denoise currently\n",
      "tensor(0.4969)\n",
      "denoise currently\n",
      "tensor(0.)\n"
     ]
    }
   ],
   "source": [
    "import torch\n",
    "\n",
    "frames = ov_pipe(\n",
    "    image,\n",
    "    num_inference_steps=4,\n",
    "    motion_bucket_id=60,\n",
    "    num_frames=8,\n",
    "    height=320,\n",
    "    width=512,\n",
    "    generator=torch.manual_seed(12342),\n",
    ").frames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5e55dee5-fbb9-4616-a4d1-14f411093bb2",
   "metadata": {},
   "outputs": [],
   "source": [
    "out_path = Path(\"generated.mp4\")\n",
    "\n",
    "export_to_video(frames, str(out_path), fps=7)\n",
    "frames[0].save(\n",
    "    \"generated.gif\",\n",
    "    save_all=True,\n",
    "    append_images=frames[1:],\n",
    "    optimize=False,\n",
    "    duration=120,\n",
    "    loop=0,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "abf5294e-d76a-496d-a5d1-0b3f7e5eafc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"generated.gif\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import HTML\n",
    "\n",
    "HTML('<img src=\"generated.gif\">')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "c042cf53",
   "metadata": {},
   "source": [
    "## Quantization\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "[NNCF](https://github.com/openvinotoolkit/nncf/) enables post-training quantization by adding quantization layers into model graph and then using a subset of the training dataset to initialize the parameters of these additional quantization layers. Quantized operations are executed in `INT8` instead of `FP32`/`FP16` making model inference faster.\n",
    "\n",
    "According to `OVStableVideoDiffusionPipeline` structure, the diffusion model takes up significant portion of the overall pipeline execution time. Now we will show you how to optimize the UNet part using [NNCF](https://github.com/openvinotoolkit/nncf/) to reduce computation cost and speed up the pipeline. Quantizing the rest of the pipeline does not significantly improve inference performance but can lead to a substantial degradation of accuracy. That's why we use only weight compression for the `vae encoder` and `vae decoder` to reduce the memory footprint.\n",
    "\n",
    "For the UNet model we apply quantization in hybrid mode which means that we quantize: (1) weights of MatMul and Embedding layers and (2) activations of other layers. The steps are the following:\n",
    "\n",
    "1. Create a calibration dataset for quantization.\n",
    "2. Collect operations with weights.\n",
    "3. Run `nncf.compress_model()` to compress only the model weights.\n",
    "4. Run `nncf.quantize()` on the compressed model with weighted operations ignored by providing `ignored_scope` parameter.\n",
    "5. Save the `INT8` model using `openvino.save_model()` function.\n",
    "\n",
    "\n",
    "Please select below whether you would like to run quantization to improve model inference speed.\n",
    "\n",
    "> **NOTE**: Quantization is time and memory consuming operation. Running quantization code below may take some time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb033895",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d56e506881dd4ade8174b280110b9454",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Checkbox(value=True, description='Quantization')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from notebook_utils import quantization_widget\n",
    "\n",
    "to_quantize = quantization_widget()\n",
    "\n",
    "to_quantize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a44c3174",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fetch `skip_kernel_extension` module\n",
    "\n",
    "ov_int8_pipeline = None\n",
    "OV_INT8_UNET_PATH = MODEL_DIR / \"unet_int8.xml\"\n",
    "OV_INT8_VAE_ENCODER_PATH = MODEL_DIR / \"vae_encoder_int8.xml\"\n",
    "OV_INT8_VAE_DECODER_PATH = MODEL_DIR / \"vae_decoder_int8.xml\"\n",
    "\n",
    "%load_ext skip_kernel_extension"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a0bfb34e",
   "metadata": {},
   "source": [
    "### Prepare calibration dataset\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "We use a portion of [`fusing/instructpix2pix-1000-samples`](https://huggingface.co/datasets/fusing/instructpix2pix-1000-samples) dataset from Hugging Face as calibration data.\n",
    "To collect intermediate model inputs for UNet optimization we should customize `CompiledModel`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "3f6093ed",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "from typing import Any\n",
    "\n",
    "import datasets\n",
    "import numpy as np\n",
    "from tqdm.notebook import tqdm\n",
    "from IPython.utils import io\n",
    "\n",
    "\n",
    "class CompiledModelDecorator(ov.CompiledModel):\n",
    "    def __init__(self, compiled_model: ov.CompiledModel, data_cache = None, keep_prob: float = 0.5):\n",
    "        super().__init__(compiled_model)\n",
    "        self.data_cache = data_cache if data_cache is not None else []\n",
    "        self.keep_prob = keep_prob\n",
    "\n",
    "    def __call__(self, *args, **kwargs):\n",
    "        if np.random.rand() <= self.keep_prob:\n",
    "            self.data_cache.append(*args)\n",
    "        return super().__call__(*args, **kwargs)\n",
    "\n",
    "\n",
    "def collect_calibration_data(ov_pipe, calibration_dataset_size: int, num_inference_steps: int = 50):\n",
    "    original_unet = ov_pipe.unet\n",
    "    calibration_data = []\n",
    "    ov_pipe.unet = CompiledModelDecorator(original_unet, calibration_data, keep_prob=1)\n",
    "\n",
    "    dataset = datasets.load_dataset(\"fusing/instructpix2pix-1000-samples\", split=\"train\", streaming=False).shuffle(seed=42)\n",
    "    # Run inference for data collection\n",
    "    pbar = tqdm(total=calibration_dataset_size)\n",
    "    for batch in dataset:\n",
    "        image = batch[\"input_image\"]\n",
    "\n",
    "        with io.capture_output() as captured:\n",
    "            ov_pipe(\n",
    "                image,\n",
    "                num_inference_steps=4,\n",
    "                motion_bucket_id=60,\n",
    "                num_frames=8,\n",
    "                height=256,\n",
    "                width=256,\n",
    "                generator=torch.manual_seed(12342),\n",
    "            )\n",
    "        pbar.update(len(calibration_data) - pbar.n)\n",
    "        if len(calibration_data) >= calibration_dataset_size:\n",
    "            break\n",
    "\n",
    "    ov_pipe.unet = original_unet\n",
    "    return calibration_data[:calibration_dataset_size]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "bfdee6ad",
   "metadata": {
    "test_replace": {
     "subset_size = 200": "subset_size = 4"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4cc257d24d904c17b93237f68dc4980b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "if not OV_INT8_UNET_PATH.exists():\n",
    "    subset_size = 200\n",
    "    calibration_data = collect_calibration_data(ov_pipe, calibration_dataset_size=subset_size)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "a054e0fa",
   "metadata": {},
   "source": [
    "### Run Hybrid Model Quantization\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2a7434b0",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "from collections import deque\n",
    "\n",
    "def get_operation_const_op(operation, const_port_id: int):\n",
    "    node = operation.input_value(const_port_id).get_node()\n",
    "    queue = deque([node])\n",
    "    constant_node = None\n",
    "    allowed_propagation_types_list = [\"Convert\", \"FakeQuantize\", \"Reshape\"]\n",
    "\n",
    "    while len(queue) != 0:\n",
    "        curr_node = queue.popleft()\n",
    "        if curr_node.get_type_name() == \"Constant\":\n",
    "            constant_node = curr_node\n",
    "            break\n",
    "        if len(curr_node.inputs()) == 0:\n",
    "            break\n",
    "        if curr_node.get_type_name() in allowed_propagation_types_list:\n",
    "            queue.append(curr_node.input_value(0).get_node())\n",
    "\n",
    "    return constant_node\n",
    "\n",
    "\n",
    "def is_embedding(node) -> bool:\n",
    "    allowed_types_list = [\"f16\", \"f32\", \"f64\"]\n",
    "    const_port_id = 0\n",
    "    input_tensor = node.input_value(const_port_id)\n",
    "    if input_tensor.get_element_type().get_type_name() in allowed_types_list:\n",
    "        const_node = get_operation_const_op(node, const_port_id)\n",
    "        if const_node is not None:\n",
    "            return True\n",
    "\n",
    "    return False\n",
    "\n",
    "\n",
    "def collect_ops_with_weights(model):\n",
    "    ops_with_weights = []\n",
    "    for op in model.get_ops():\n",
    "        if op.get_type_name() == \"MatMul\":\n",
    "            constant_node_0 = get_operation_const_op(op, const_port_id=0)\n",
    "            constant_node_1 = get_operation_const_op(op, const_port_id=1)\n",
    "            if constant_node_0 or constant_node_1:\n",
    "                ops_with_weights.append(op.get_friendly_name())\n",
    "        if op.get_type_name() == \"Gather\" and is_embedding(op):\n",
    "            ops_with_weights.append(op.get_friendly_name())\n",
    "\n",
    "    return ops_with_weights"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1ef9c787",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/openvino/runtime/__init__.py:10: DeprecationWarning: The `openvino.runtime` module is deprecated and will be removed in the 2026.0 release. Please replace `openvino.runtime` with `openvino`.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0d9a4adebc36497ab3c123419ea46ca4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1ace1aa570eb4f008b4629402bd24a88",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e25d69a947f4eecb3e73bd813bcb44b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "import nncf\n",
    "import logging\n",
    "from nncf.quantization.advanced_parameters import AdvancedSmoothQuantParameters\n",
    "\n",
    "nncf.set_log_level(logging.ERROR)\n",
    "\n",
    "if not OV_INT8_UNET_PATH.exists():\n",
    "    diffusion_model = core.read_model(UNET_PATH)\n",
    "    unet_ignored_scope = collect_ops_with_weights(diffusion_model)\n",
    "    compressed_diffusion_model = nncf.compress_weights(diffusion_model, ignored_scope=nncf.IgnoredScope(types=['Convolution']))\n",
    "    quantized_diffusion_model = nncf.quantize(\n",
    "        model=compressed_diffusion_model,\n",
    "        calibration_dataset=nncf.Dataset(calibration_data),\n",
    "        subset_size=subset_size,\n",
    "        model_type=nncf.ModelType.TRANSFORMER,\n",
    "        # We additionally ignore the first convolution to improve the quality of generations\n",
    "        ignored_scope=nncf.IgnoredScope(names=unet_ignored_scope + [\"__module.conv_in/aten::_convolution/Convolution\"]),\n",
    "        advanced_parameters=nncf.AdvancedQuantizationParameters(smooth_quant_alphas=AdvancedSmoothQuantParameters(matmul=-1))\n",
    "    )\n",
    "    ov.save_model(quantized_diffusion_model, OV_INT8_UNET_PATH)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "17705d82",
   "metadata": {},
   "source": [
    "### Run Weight Compression\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Quantizing of the `vae encoder` and `vae decoder` does not significantly improve inference performance but can lead to a substantial degradation of accuracy. Only weight compression will be applied for footprint reduction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "f9f4a468",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:nncf:Statistics of the bitwidth distribution:\n",
      "┍━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑\n",
      "│ Weight compression mode   │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │\n",
      "┝━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥\n",
      "│ int8_asym                 │ 98% (29 / 32)               │ 0% (0 / 3)                             │\n",
      "├───────────────────────────┼─────────────────────────────┼────────────────────────────────────────┤\n",
      "│ int4_sym                  │ 2% (3 / 32)                 │ 100% (3 / 3)                           │\n",
      "┕━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ac8069704ed8444388b07255645109d4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:nncf:Statistics of the bitwidth distribution:\n",
      "┍━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┯━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┑\n",
      "│ Weight compression mode   │ % all parameters (layers)   │ % ratio-defining parameters (layers)   │\n",
      "┝━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┿━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┥\n",
      "│ int8_asym                 │ 99% (65 / 68)               │ 0% (0 / 3)                             │\n",
      "├───────────────────────────┼─────────────────────────────┼────────────────────────────────────────┤\n",
      "│ int4_sym                  │ 1% (3 / 68)                 │ 100% (3 / 3)                           │\n",
      "┕━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┷━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┙\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad2e3403a8c24dad9a9ed847d4653dd6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "nncf.set_log_level(logging.INFO)\n",
    "\n",
    "if not OV_INT8_VAE_ENCODER_PATH.exists():\n",
    "    text_encoder_model = core.read_model(VAE_ENCODER_PATH)\n",
    "    compressed_text_encoder_model = nncf.compress_weights(text_encoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)\n",
    "    ov.save_model(compressed_text_encoder_model, OV_INT8_VAE_ENCODER_PATH)\n",
    "\n",
    "if not OV_INT8_VAE_DECODER_PATH.exists():\n",
    "    decoder_model = core.read_model(VAE_DECODER_PATH)\n",
    "    compressed_decoder_model = nncf.compress_weights(decoder_model, mode=nncf.CompressWeightsMode.INT4_SYM, group_size=64)\n",
    "    ov.save_model(compressed_decoder_model, OV_INT8_VAE_DECODER_PATH)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "9026878f",
   "metadata": {},
   "source": [
    "Let's compare the video generated by the original and optimized pipelines. Dynamic quantization should be disabled for UNet model because it introduces a performance overhead when applied to Diffusion models that have been quantized using a `Hybrid` approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "b3156d0b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ebff9af28dc1429fb392fd15d8b32d0b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ea/work/py311/lib/python3.11/site-packages/diffusers/configuration_utils.py:141: FutureWarning: Accessing config attribute `unet` directly via 'OVStableVideoDiffusionPipeline' object attribute is deprecated. Please access 'unet' over 'OVStableVideoDiffusionPipeline's config object instead, e.g. 'scheduler.config.unet'.\n",
      "  deprecate(\"direct config name access\", \"1.0.0\", deprecation_message, standard_warn=False)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "denoise currently\n",
      "tensor(128.5637)\n",
      "denoise currently\n",
      "tensor(13.6784)\n",
      "denoise currently\n",
      "tensor(0.4969)\n",
      "denoise currently\n",
      "tensor(0.)\n"
     ]
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "ov_int8_vae_encoder = core.compile_model(OV_INT8_VAE_ENCODER_PATH, device.value)\n",
    "ov_int8_unet = core.compile_model(OV_INT8_UNET_PATH, device.value, config={\"DYNAMIC_QUANTIZATION_GROUP_SIZE\":\"0\"})\n",
    "ov_int8_decoder = core.compile_model(OV_INT8_VAE_DECODER_PATH, device.value)\n",
    "\n",
    "ov_int8_pipeline = OVStableVideoDiffusionPipeline(\n",
    "    ov_int8_vae_encoder, image_encoder, ov_int8_unet, ov_int8_decoder, scheduler, feature_extractor\n",
    ")\n",
    "\n",
    "int8_frames = ov_int8_pipeline(\n",
    "    image,\n",
    "    num_inference_steps=4,\n",
    "    motion_bucket_id=60,\n",
    "    num_frames=8,\n",
    "    height=320,\n",
    "    width=512,\n",
    "    generator=torch.manual_seed(12342),\n",
    ").frames[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "902036a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"generated_int8.gif\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "from IPython.display import display\n",
    "\n",
    "int8_out_path = Path(\"generated_int8.mp4\")\n",
    "\n",
    "export_to_video(int8_frames, str(int8_out_path), fps=7)\n",
    "int8_frames[0].save(\n",
    "    \"generated_int8.gif\",\n",
    "    save_all=True,\n",
    "    append_images=int8_frames[1:],\n",
    "    optimize=False,\n",
    "    duration=120,\n",
    "    loop=0,\n",
    ")\n",
    "display(HTML('<img src=\"generated_int8.gif\">'))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "b223a0a7",
   "metadata": {},
   "source": [
    "### Compare model file sizes\n",
    "\n",
    "[back to top ⬆️](#Table-of-contents:)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7099c21b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "vae_encoder compression rate: 2.018\n",
      "unet compression rate: 1.996\n",
      "vae_decoder compression rate: 2.007\n"
     ]
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "fp16_model_paths = [VAE_ENCODER_PATH, UNET_PATH, VAE_DECODER_PATH]\n",
    "int8_model_paths = [OV_INT8_VAE_ENCODER_PATH, OV_INT8_UNET_PATH, OV_INT8_VAE_DECODER_PATH]\n",
    "\n",
    "for fp16_path, int8_path in zip(fp16_model_paths, int8_model_paths):\n",
    "    fp16_ir_model_size = fp16_path.with_suffix(\".bin\").stat().st_size\n",
    "    int8_model_size = int8_path.with_suffix(\".bin\").stat().st_size\n",
    "    print(f\"{fp16_path.stem} compression rate: {fp16_ir_model_size / int8_model_size:.3f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "65cec1b7",
   "metadata": {},
   "source": [
    "### Compare inference time of the FP16 and INT8 pipelines\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "To measure the inference performance of the `FP16` and `INT8` pipelines, we use median inference time on calibration subset.\n",
    "\n",
    "> **NOTE**: For the most accurate performance estimation, it is recommended to run `benchmark_app` in a terminal/command prompt after closing other applications."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "80d1b146",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "import time\n",
    "\n",
    "def calculate_inference_time(pipeline, validation_data):\n",
    "    inference_time = []\n",
    "    for prompt in validation_data:\n",
    "        start = time.perf_counter()\n",
    "        with io.capture_output() as captured:\n",
    "            _ = pipeline(\n",
    "                image,\n",
    "                num_inference_steps=4,\n",
    "                motion_bucket_id=60,\n",
    "                num_frames=8,\n",
    "                height=320,\n",
    "                width=512,\n",
    "                generator=torch.manual_seed(12342),\n",
    "            )\n",
    "        end = time.perf_counter()\n",
    "        delta = end - start\n",
    "        inference_time.append(delta)\n",
    "    return np.median(inference_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "438d896c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performance speed-up: 1.313\n"
     ]
    }
   ],
   "source": [
    "%%skip not $to_quantize.value\n",
    "\n",
    "validation_size = 3\n",
    "validation_dataset = datasets.load_dataset(\"fusing/instructpix2pix-1000-samples\", split=\"train\", streaming=True).shuffle(seed=42).take(validation_size)\n",
    "validation_data = [data[\"input_image\"] for data in validation_dataset]\n",
    "\n",
    "fp_latency = calculate_inference_time(ov_pipe, validation_data)\n",
    "int8_latency = calculate_inference_time(ov_int8_pipeline, validation_data)\n",
    "print(f\"Performance speed-up: {fp_latency / int8_latency:.3f}\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "f85c9cf6-8b88-462f-86bf-d5df450d82c2",
   "metadata": {},
   "source": [
    "## Interactive Demo\n",
    "[back to top ⬆️](#Table-of-contents:)\n",
    "\n",
    "Please select below whether you would like to use the quantized model to launch the interactive demo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "840decf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a79a169b6f4345789aef0e86b3ecf062",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Checkbox(value=True, description='Use quantized model')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "\n",
    "quantized_model_present = ov_int8_pipeline is not None\n",
    "\n",
    "use_quantized_model = widgets.Checkbox(\n",
    "    value=quantized_model_present,\n",
    "    description=\"Use quantized model\",\n",
    "    disabled=not quantized_model_present,\n",
    ")\n",
    "\n",
    "use_quantized_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e1fe35f3-4f07-4ebd-9a1e-ae0431450c07",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "* Running on local URL:  http://127.0.0.1:7860\n",
      "Rerunning server... use `close()` to stop if you need to change `launch()` parameters.\n",
      "----\n",
      "* Running on public URL: https://daecebba75c1cc7ad0.gradio.live\n",
      "\n",
      "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><iframe src=\"https://daecebba75c1cc7ad0.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Keyboard interruption in main thread... closing server.\n",
      "Killing tunnel 127.0.0.1:7860 <> https://daecebba75c1cc7ad0.gradio.live\n"
     ]
    }
   ],
   "source": [
    "if not Path(\"gradio_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/stable-video-diffusion/gradio_helper.py\")\n",
    "    open(\"gradio_helper.py\", \"w\").write(r.text)\n",
    "\n",
    "from gradio_helper import make_demo\n",
    "\n",
    "pipeline = ov_int8_pipeline if use_quantized_model.value else ov_pipe\n",
    "\n",
    "demo = make_demo(pipeline)\n",
    "\n",
    "try:\n",
    "    demo.queue().launch(debug=True)\n",
    "except Exception:\n",
    "    demo.queue().launch(debug=True, share=True)\n",
    "# if you are launching remotely, specify server_name and server_port\n",
    "# demo.launch(server_name='your server name', server_port='server port in int')\n",
    "# Read more in the docs: https://gradio.app/docs/"
   ]
  }
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